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Indoor cultivation of Cannabis sativa is essential for precise environmental control, maximizing cannabinoid yield, ensuring year-round product availability, and allowing genetic enhancement, making it indispensable for commercial producers and cannabis consumers alike. SmartGrow DataControl presents a cutting-edge computational tool designed specifically for monitoring, collecting, and controlling microclimatic factors in indoor crop cultivation. This system utilizes Model-View-Controller (MVC) architecture patterns and layered architectural patterns to seamlessly combine wireless devices with open-source databases. It focuses on energy efficiency by implementing standardized communication protocols like as TCP/IP, MQTT, HTTP, and Web Socket. The platform intends to optimize data collection for agricultural physiology studies, specifically in crops like Cannabis sativa, by utilizing advanced optical and Internet of Things (IoT) technologies. It is designed with a user-friendly interface to facilitate ease of use. Experimental validation verifies the effectiveness of SmartGrow DataControl, enabling farmers to effectively oversee crops and increase productivity through gradual technical improvements.

When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions of data packets received from several sources may occur, which lead to information loss. With an increase in the number of simultaneously operating hydroacoustic modems, physical layer algorithms do not provide stable data transmission and the likelihood of collisions increases, which makes the operation of modems ineffective. To ensure effective operation in a hydroacoustic signal propagation environment and to reduce collisions when exchanging data between two modems that do not have the ability to operate synchronously and to reduce the access time to the signal propagation environment, methods of the medium access control layer using link layer protocols are required. Typically, this problem is solved using code separation of hydroacoustic channels. If you need to transfer over a network, this option will not work, since network transfer involves working on the basis of "broadcast" messages, particularly between data source and data sink that remain too far from each other, outside of their mutual audibility. In practical use, it is convenient to place these protocols into a software environment for developing specific user applications for solving network communication problems. This software framework allows for custom modification of existing network algorithms, as well as the inclusion of new network hydroacoustic communication algorithms. To build a predictive model, the DACAP, T-Lohi, Flooding, and ICRP protocols were used in this work. The implementation is performed in Erlang. The paper presents algorithms for implementing these protocols. A comparative analysis of network operation with and without protocols is provided. Efficiency of operation, i.e., data rates and probabilities of data delivery, was assessed. [ABSTRACT FROM AUTHOR]

Blockchain applications in healthcare have grown rapidly and it includes record keeping, clinical trials, medical supply chains, patient monitoring, etc. where Blockchain characteristics are needed to improve safety, privacy, and security. Blockchain Technology is one of the biggest disruptive technologies today. However, Porru, et. al (2017) have reported it lacks processes, tools, and techniques. Therefore, this paper provides a systematic framework for a secure and sustainable software engineering framework for healthcare blockchain applications. Secure and Sustainable Software Engineering Framework for Healthcare Blockchain Applications (S3EF-HBCA) significant contribution includes requirements engineering for healthcare, business process modelling for healthcare, domain modelling for healthcare, a reference architecture for healthcare, and validation by case study on Electronic Healthcare Record Management System (EHR), and simulation with Business Process Modelling Notation (BPMN) tools. The simulation shows it has taken 10.45 minutes to process 100 instances of real-time data and service requests. The overall result shows encouragement in terms of process, tools, standards, and testing.

OptiGUI DataCollector is a Python 3.8-based graphical user interface that facilitates automated data collection in optics and photonics research and development equipment. It provides an intuitive and easy-to-use platform for controlling a wide range of optical instruments, including spectrometers and lasers. OptiGUI DataCollector is a flexible and modular framework that enables simple integration with different types of devices. It simplifies experimental workflow and reduces human error by automating parameter control, data acquisition, and analysis. OptiGUI DataCollector is currently focused on optical mode conversion utilizing fiber optic technologies but can be expanded to other R&D processes.

When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions of data packets received from several sources may occur, which lead to information loss. With an increase in the number of simultaneously operating hydroacoustic modems, physical layer algorithms do not provide stable data transmission and the likelihood of collisions increases, which makes the operation of modems ineffective. To ensure effective operation in a hydroacoustic signal propagation environment and to reduce collisions when exchanging data between two modems that do not have the ability to operate synchronously and to reduce the access time to the signal propagation environment, methods of the medium access control layer using link layer protocols are required. Typically, this problem is solved using code separation of hydroacoustic channels. If you need to transfer over a network, this option will not work, since network transfer involves working on the basis of “broadcast” messages, particularly between data source and data sink that remain too far from each other, outside of their mutual audibility. In practical use, it is convenient to place these protocols into a software environment for developing specific user applications for solving network communication problems. This software framework allows for custom modification of existing network algorithms, as well as the inclusion of new network hydroacoustic communication algorithms. To build a predictive model, the DACAP, T-Lohi, Flooding, and ICRP protocols were used in this work. The implementation is performed in Erlang. The paper presents algorithms for implementing these protocols. A comparative analysis of network operation with and without protocols is provided. Efficiency of operation, i.e., data rates and probabilities of data delivery, was assessed.

Real-time analysis of large multimedia streams is nowadays made efficient by the existence of several Big Data streaming platforms, like Apache Flink and Samza. However, the use of such platforms is difficult due to the fact that facilities they offer are often too raw to be effectively exploited by analysts. We describe the evolution of RAM3S, a software infrastructure for the integration of Big Data stream processing platforms, to SPAF, an abstraction framework able to provide programmers with a simple but powerful API to ease the development of stream processing applications. By using SPAF, the programmer can easily implement real-time complex analyses of massive streams on top of a distributed computing infrastructure, able to manage the volume and velocity of Big Data streams, thus effectively transforming data into value.

Existing software tools for specifying and executing multidrone missions are limited to route planning or tightly coupled to specific drone hardware. We introduce EAMOS (Execution of Aerial Multidrone Missions and Operations Specification Framework), which allows us to specify missions intuitively, text-based, and provides a mission compiler, a mission middle layer, and a distributed drone execution environment. The middle layer wraps the control of individual drone-specific capabilities, such as launch, fly to position, or perform a maneuver, into a public API that transparently utilizes the capabilities of numerous drone platforms. We exploit the Go programming language to implement critical components of the framework and provide an interface for ROS-based drone platforms. EAMOS automates the mission execution on real, virtual, and even hybrid robotic setups involving real and virtual drones. We demonstrate the successful deployment of EAMOS with four missions executed on Pixhawk/PX4-equipped quadcopters and virtual drones simulated with Airsim. We assess the performance of our proposed approach by analyzing the number of nodes and arcs of the mission graphs, which are an essential artifact of our mission compilation, the utilization of ROS service calls during mission execution, and the duration of compilation, deployment, and mission execution. Overall, our experiments showed that our drones correctly behaved during mission execution as expected and specified by their mission, the generated mission artifacts were efficiently manageable, and processing times allowed for a fluent workflow.

The demand for deep learning frameworks capable of running in edge computing environments is rapidly increasing due to the exponential growth of data volume and the need for real-time processing. However, edge computing environments often have limited resources, necessitating the distribution of deep learning models. Distributing deep learning models can be challenging as it requires specifying the resource type for each process and ensuring that the models are lightweight without performance degradation. To address this issue, we propose the Microservice Deep-learning Edge Detection (MDED) framework, designed for easy deployment and distributed processing in edge computing environments. The MDED framework leverages Docker-based containers and Kubernetes orchestration to obtain a pedestrian-detection deep learning model with a speed of up to 19 FPS, satisfying the semi-real-time condition. The framework employs an ensemble of high-level feature-specific networks (HFN) and low-level feature-specific networks (LFN) trained on the MOT17Det dataset, achieving an accuracy improvement of up to AP50 and AP0.18 on MOT20Det data. [ABSTRACT FROM AUTHOR]

Production planning poses major challenges due to high product diversity, high fluctuations in demand and strong dynamic fluctuations in delivery quantities and times. This makes automation of production planning increasingly necessary to enable optimized sequence planning based on current production data. Production control systems (PRS) offer the possibility of automatic corrective actions in the event of deviations from the specified target dates. Production, PRS and data acquisition are considered as a single control loop and are the subject of various development and research topics. However, automatic interventions in planning and control can lead to serious disruptions if there are errors in the software of the PRS. Therefore, testing and validation of PRS is a crucial factor in development, but is hardly possible in real production environments due to the availability of physical manufacturing facilities and the associated risks. Therefore, in the scope of the paper, a concept for a software framework is proposed to provide a test environment for a PRS. A virtual production environment, which can be combined with the PRS and parameterized in form of a discrete-event simulation, offers the possibility to test the PRS. It is also possible to couple the simulation with a reference calculation to show the behavior of the production environment. The entire framework is designed to enable the identification of critical machines and times as well as, in further steps, the detection of software errors in the PRS. [ABSTRACT FROM AUTHOR]

To address the new functional requirements brought by the introduction of new weapons and new combat modes, a comprehensive survey of the research progress in the area of combat simulation software is performed from the perspective of software engineering. First, the top-level specification, simulation engine, and simulation framework of combat simulation software are reviewed. Then, several typical combat simulation software systems are demonstrated, and the relevant software frameworks are analyzed. Finally, combining the application prospect of artificial intelligence, metaverse, and other new technologies in combat simulation, the development trends of combat simulation software are presented, namely intellectualization, adaptation to an LVC (live, virtual, and constructive) system, and a more game-based experience. Based on a comprehensive comparison between the mentioned simulation frameworks, we believe that the AFSIM (Advanced framework for simulation, integration, and modeling) and the E-CARGO (Environments—classes, agents, roles, groups, and objects) are appropriate candidates for developing distributed combat simulation software. [ABSTRACT FROM AUTHOR]

Motivation: Phylogenetic trees are often inferred from a multiple sequence alignment (MSA) where the tree accuracy is heavily impacted by the nature of estimated alignment. Carefully equipping an MSA tool with multiple application-aware objectives positively impacts its capability to yield better trees. Results: We introduce Multiobjective Application-aware Multiple Sequence Alignment and Maximum Likelihood Ensemble (MAMMLE), a framework for inferring better phylogenetic trees from unaligned sequences by hybridizing two MSA tools [i.e., Multiple Sequence Comparison by Log-Expectation (MUSCLE) and Multiple Alignment using Fast Fourier Transform (MAFFT)] with multiobjective optimization strategy and leveraging multiple maximum likelihood hypotheses. In our experiments, MAMMLE exhibits 5.57% (4.77%) median improvement (deterioration) over MUSCLE on 50.34% (37.41%) of instances. [ABSTRACT FROM AUTHOR]

In this article, we present a framework, called Rehab-Immersive (RI), for the development of virtual reality clinical applications as a complement to the rehabilitation of patients with spinal cord injuries. RI addresses the interaction of patients with virtual worlds, considering upper limb motor impairments. A preconfiguration allows customization for each patient’s specific needs. RI also stores kinematics data, providing clinical staff with a valuable tool to evaluate progress and patient exercise performance. As an example, a virtual version of the Box & Block test is presented.

Recent advances in reinforcement learning (RL) have increased the promise of introducing cognitive assistance and automation to robot-assisted laparoscopic surgery (RALS). However, progress in algorithms and methods depends on the availability of standardized learning environments that represent skills relevant to RALS.We present LapGym, a framework for building RL environments for RALS that models the challenges posed by surgical tasks, and sofa env, a diverse suite of 12 environments. Motivated by surgical training, these environments are organized into 4 tracks: Spatial Reasoning, Deformable Object Manipulation & Grasping, Dissection, and Thread Manipulation. Each environment is highly parametrizable for increasing difficulty, resulting in a high performance ceiling for new algorithms. We use Proximal Policy Optimization (PPO) to establish a baseline for model-free RL algorithms, investigating the effect of several environment parameters on task difficulty. Finally, we show that many environments and parameter configurations re ect well-known, open problems in RL research, allowing researchers to continue exploring these fundamental problems in a surgical context. We aim to provide a challenging, standard environment suite for further development of RL for RALS, ultimately helping to realize the full potential of cognitive surgical robotics. LapGym is publicly accessible through GitHub (https://github.com/ScheiklP/lap_gym). [ABSTRACT FROM AUTHOR]

IoT technology applied to agriculture has produced a number of contributions in the recent years. Such solutions are, most of the time, fully tailored to a particular functional target and focus extensively on sensor-hardware development and customization. As a result, software-centered solutions for IoT system development are infrequent. This is not suitable, as the software is the bottleneck in modern computer systems, being the main source of performance loss, errors, and even cyber attacks. This paper takes a software-centric perspective to model and design IoT systems in a flexible manner. We contribute a software framework that supports the design of the IoT systems' software based on software services in a client–server model with REST interactions; and it is exemplified on the domain of efficient irrigation in agriculture. We decompose the services' design into the set of constituent functions and operations both at client and server sides. As a result, we provide a simple and novel view on the design of IoT systems in agriculture from a sofware perspective: we contribute simple design structure based on the identification of the front-end software services, their internal software functions and operations, and their interconnections as software services. We have implemented the software framework on an IoT irrigation use case that monitors the conditions of the field and processes the sampled data, detecting alarms when needed. We demonstrate that the temporal overhead of our solution is bounded and suitable for the target domain, reaching a response time of roughly 11 s for bursts of 3000 requests. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) devices are mostly ubiquitous in this day and age, and it is hard to imagine a life without them, especially in the productive sectors (industry, agriculture, and automotive) and in our daily life activities (consumer electronics, home automation, and intelligent buildings). The high demand for these devices has created significant competition to provide them at the best price, at the right time, and with the best features. The trend in which these devices have increased their product features has resulted in their embedded software being more complex, leading to extended development and testing times. Consequently, as the types of advanced IoT products keep diversifying, the field maintenance of all the different models deployed grows more complicated. This paper proposes an embedded software development framework for IoT devices independent of the microcontroller architecture, the compiler, and the development environment. This framework allows having a common software baseline between different projects, which shortens the learning curve, development time, and module validation while allowing code reuse for embedded software professionals. A proof-of-concept evaluation is also presented to demonstrate the efficiency and reliability of the obtained embedded software code for a simple but representative IoT application. [ABSTRACT FROM AUTHOR]

Machine vision systems are an important part of modern intelligent manufacturing systems, but due to their complexity, current vision systems are often customized and inefficiently developed. Generic closed-source machine vision development software is often poorly targeted. To meet the extensive needs of product appearance quality inspection in industrial production and to improve the development efficiency and reliability of such systems, this paper designs and implements a general machine vision software framework. This framework is easy to adapt to different hardware devices for secondary development, reducing the workload in generic functional modules and program architecture design, which allows developers to focus on the design and implementation of image-processing algorithms. Based on the MVP software design principles, the framework abstracts and implements the modules common to machine vision-based product appearance quality inspection systems, such as user management, inspection configuration, task management, image acquisition, database configuration, GUI, multi-threaded architecture, IO communication, etc. Using this framework and adding the secondary development of image-processing algorithms, we successfully apply the framework to the quality inspection of the surface defects of bolts. [ABSTRACT FROM AUTHOR]

The concept of coding at school has enabled educators and parents around the globe to become interested in coding as, these days, coding is regarded as a gateway to computational thinking for children. However, coding education in Thailand appears to lag behind that of many other countries due to the lack of accessible coding learning resources suitable for students as well as the limited number of teachers with coding experience. Regarding these issues, we have developed an open-source hardware-based coding platform named KidBright, based on these requirements: 1. making coding simple for novice learners through the use of graphical blocks with Thai and English support; 2. inspiring young students to develop creative embedded applications with minimal effort; and 3. providing sustainable support for coding education. KidBright is proposed as a coding learning tool that can motivate children to learn to code and develop embedded system projects using its block-based coding environment, the KidBright IDE, in conjunction with its embedded device, the KidBright board. KidBright is distinguished from other embedded programming platforms due to the deployment of a dedicated software framework as the backend of the KidBright IDE. In this article, we introduce KidBright and present the design, architecture, and demonstrations of the software framework, the key roles of which are to conceal low-level hardware issues from learners and to enable makers to develop new command blocks and hardware peripherals in support of KidBright. We claim that, with this particular design, KidBright can help support coding education sustainably. In particular, we present how a small research team introduces coding to a large number of learners who have little or no coding experience, resulting in an impact on coding education in the country. [ABSTRACT FROM AUTHOR]

Abstract Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort.

The demand for deep learning frameworks capable of running in edge computing environments is rapidly increasing due to the exponential growth of data volume and the need for real-time processing. However, edge computing environments often have limited resources, necessitating the distribution of deep learning models. Distributing deep learning models can be challenging as it requires specifying the resource type for each process and ensuring that the models are lightweight without performance degradation. To address this issue, we propose the Microservice Deep-learning Edge Detection (MDED) framework, designed for easy deployment and distributed processing in edge computing environments. The MDED framework leverages Docker-based containers and Kubernetes orchestration to obtain a pedestrian-detection deep learning model with a speed of up to 19 FPS, satisfying the semi-real-time condition. The framework employs an ensemble of high-level feature-specific networks (HFN) and low-level feature-specific networks (LFN) trained on the MOT17Det dataset, achieving an accuracy improvement of up to AP50 and AP0.18 on MOT20Det data.

To address the new functional requirements brought by the introduction of new weapons and new combat modes, a comprehensive survey of the research progress in the area of combat simulation software is performed from the perspective of software engineering. First, the top-level specification, simulation engine, and simulation framework of combat simulation software are reviewed. Then, several typical combat simulation software systems are demonstrated, and the relevant software frameworks are analyzed. Finally, combining the application prospect of artificial intelligence, metaverse, and other new technologies in combat simulation, the development trends of combat simulation software are presented, namely intellectualization, adaptation to an LVC (live, virtual, and constructive) system, and a more game-based experience. Based on a comprehensive comparison between the mentioned simulation frameworks, we believe that the AFSIM (Advanced framework for simulation, integration, and modeling) and the E-CARGO (Environments—classes, agents, roles, groups, and objects) are appropriate candidates for developing distributed combat simulation software.

This paper presents a new software framework, Sampo-UI, for developing user interfaces for semantic portals. The goal is to provide the end-user with multiple application perspectives to Linked Data knowledge graphs, and a two-step usage cycle based on faceted search combined with ready-to-use tooling for data analysis. For the software developer, the Sampo-UI framework makes it possible to create highly customizable, user-friendly, and responsive user interfaces using current state-of-the-art JavaScript libraries and data from SPARQL endpoints, while saving substantial coding effort. Sampo-UI is published on GitHub under the open MIT License and has been utilized in several internal and external projects. The framework has been used thus far in creating six published and five forth-coming portals, mostly related to the Cultural Heritage domain, that have had tens of thousands of end-users on the Web. [ABSTRACT FROM AUTHOR]

Computational models of emotion (CMEs) are software systems designed to emulate specific aspects of the human emotions process. The underlying components of CMEs interact with cognitive components of cognitive agent architectures to produce realistic behaviors in intelligent agents. However, in contemporary CMEs, the interaction between affective and cognitive components occurs in an ad-hoc manner, which leads to difficulties when new affective or cognitive components should be added in the CME. This paper presents a framework that facilitates taking into account in CMEs the cognitive information generated by cognitive components implemented in cognitive agent architectures. The framework is designed to allow researchers to define how cognitive information biases the internal workings of affective components. This framework is inspired in software interoperability practices to enable communication and interpretation of cognitive information and standardize the cognitive-affective communication process by ensuring semantic communication channels used to modulate affective mechanisms of CMEs. [ABSTRACT FROM AUTHOR]

Recent advances in artificial intelligence (AI), driven mainly by deep neural networks, have yielded remarkable progress in fields, such as computer vision, natural language processing, and reinforcement learning. Despite these successes, the inability to predict how AI systems will behave "in the wild" impacts almost all stages of planning and deployment, including research and development, verification and validation, and user trust and acceptance. The field of explainable artificial intelligence (XAI) seeks to develop techniques enabling AI algorithms to generate explanations of their results; generally these are human-interpretable representations or visualizations that are meant to "explain" how the system produced its outputs. We introduce the Explainable AI Toolkit (XAITK), a DARPA-sponsored effort that builds on results from the 4-year DARPA XAI program. The XAITK has two goals: (a) to consolidate research results from DARPA XAI into a single publicly accessible repository; and (b) to identify operationally relevant capabilities developed on DARPA XAI and assist in their transition to interested partners. We first describe the XAITK website and associated capabilities. These place the research results from DARPA XAI in the wider context of general research in the field of XAI, and include performer contributions of code, data, publications, and reports. We then describe the XAITK analytics and autonomy software frameworks. These are Python-based frameworks focused on particular XAI domains, and designed to provide a single integration endpoint for multiple algorithm implementations from across DARPA XAI. Each framework generalizes APIs for system-level data and control while providing a plugin interface for existing and future algorithm implementations. The XAITK project can be followed at: https://xaitk.org. [ABSTRACT FROM AUTHOR]

Abstract Recent advances in artificial intelligence (AI), driven mainly by deep neural networks, have yielded remarkable progress in fields, such as computer vision, natural language processing, and reinforcement learning. Despite these successes, the inability to predict how AI systems will behave “in the wild” impacts almost all stages of planning and deployment, including research and development, verification and validation, and user trust and acceptance. The field of explainable artificial intelligence (XAI) seeks to develop techniques enabling AI algorithms to generate explanations of their results; generally these are human‐interpretable representations or visualizations that are meant to “explain” how the system produced its outputs. We introduce the Explainable AI Toolkit (XAITK), a DARPA‐sponsored effort that builds on results from the 4‐year DARPA XAI program. The XAITK has two goals: (a) to consolidate research results from DARPA XAI into a single publicly accessible repository; and (b) to identify operationally relevant capabilities developed on DARPA XAI and assist in their transition to interested partners. We first describe the XAITK website and associated capabilities. These place the research results from DARPA XAI in the wider context of general research in the field of XAI, and include performer contributions of code, data, publications, and reports. We then describe the XAITK analytics and autonomy software frameworks. These are Python‐based frameworks focused on particular XAI domains, and designed to provide a single integration endpoint for multiple algorithm implementations from across DARPA XAI. Each framework generalizes APIs for system‐level data and control while providing a plugin interface for existing and future algorithm implementations. The XAITK project can be followed at: https://xaitk.org.

IoT technology applied to agriculture has produced a number of contributions in the recent years. Such solutions are, most of the time, fully tailored to a particular functional target and focus extensively on sensor-hardware development and customization. As a result, software-centered solutions for IoT system development are infrequent. This is not suitable, as the software is the bottleneck in modern computer systems, being the main source of performance loss, errors, and even cyber attacks. This paper takes a software-centric perspective to model and design IoT systems in a flexible manner. We contribute a software framework that supports the design of the IoT systems’ software based on software services in a client–server model with REST interactions; and it is exemplified on the domain of efficient irrigation in agriculture. We decompose the services’ design into the set of constituent functions and operations both at client and server sides. As a result, we provide a simple and novel view on the design of IoT systems in agriculture from a sofware perspective: we contribute simple design structure based on the identification of the front-end software services, their internal software functions and operations, and their interconnections as software services. We have implemented the software framework on an IoT irrigation use case that monitors the conditions of the field and processes the sampled data, detecting alarms when needed. We demonstrate that the temporal overhead of our solution is bounded and suitable for the target domain, reaching a response time of roughly 11 s for bursts of 3000 requests.

Model Predictive Control (MPC) can be used in the context of building automation to improve energy efficiency and occupant comfort. Ideally, the MPC algorithm should consider current- and planned usage of the controlled environment along with initial state and weather forecast to plan for optimal comfort and energy efficiency. This implies the need for an MPC application which 1) considers multiple objectives, 2) can draw on multiple data sources, and 3) provides an approach to effectively integrate against heterogeneous building automation systems to make the approach reusable across different installations. To this end, this paper presents a design and implementation of a framework for digital twins for buildings in which the controlled environments are represented as digital entities. In this framework, digital twins constitute parametrized models which are integrated into a generic control algorithm that uses data on weather forecasts, current- and planned occupancy as well as the current state of the controlled environment to perform MPC. This data is accessed through a generic data layer to enable uniform data access. This enables the framework to switch seamlessly between simulation and real-life applications and reduces the barrier towards reusing the application in a different control environment. We demonstrate an application of the digital twin framework on a case study at the University of Southern Denmark where a digital twin has been used to control heating and ventilation. From the case study, we observe that we can switch from rule-based control to model predictive control with no immediate adverse effects towards comfort or energy consumption. We also identify the potential for an increase in energy efficiency, as well as introduce the possibility of planning energy consumption against local electricity production or market conditions, while maintaining occupant comfort. [ABSTRACT FROM AUTHOR]

Machine vision systems are an important part of modern intelligent manufacturing systems, but due to their complexity, current vision systems are often customized and inefficiently developed. Generic closed-source machine vision development software is often poorly targeted. To meet the extensive needs of product appearance quality inspection in industrial production and to improve the development efficiency and reliability of such systems, this paper designs and implements a general machine vision software framework. This framework is easy to adapt to different hardware devices for secondary development, reducing the workload in generic functional modules and program architecture design, which allows developers to focus on the design and implementation of image-processing algorithms. Based on the MVP software design principles, the framework abstracts and implements the modules common to machine vision-based product appearance quality inspection systems, such as user management, inspection configuration, task management, image acquisition, database configuration, GUI, multi-threaded architecture, IO communication, etc. Using this framework and adding the secondary development of image-processing algorithms, we successfully apply the framework to the quality inspection of the surface defects of bolts.

The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data.

The popularity of mesoscopic whole-brain imaging techniques has increased dramatically, but these techniques generate teravoxel-sized volumetric image data. Visualizing or interacting with these massive data is both necessary and essential in the bioimage analysis pipeline; however, due to their size, researchers have difficulty using typical computers to process them. The existing solutions do not consider applying web visualization and three-dimensional (3D) volume rendering methods simultaneously to reduce the number of data copy operations and provide a better way to visualize 3D structures in bioimage data. Here, we propose webTDat, an open-source, web-based, real-time 3D visualization framework for mesoscopic-scale whole-brain imaging datasets. webTDat uses an advanced rendering visualization method designed with an innovative data storage format and parallel rendering algorithms. webTDat loads the primary information in the image first and then decides whether it needs to load the secondary information in the image. By performing validation on TB-scale whole-brain datasets, webTDat achieves real-time performance during web visualization. The webTDat framework also provides a rich interface for annotation, making it a useful tool for visualizing mesoscopic whole-brain imaging data. [ABSTRACT FROM AUTHOR]

The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit these requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack a unified vision of these techniques, and a methodological workflow that supports their usage. Hence, we present the Sherpa.ai Federated Learning framework that is built upon a holistic view of federated learning and differential privacy. It results from both the study of how to adapt the machine learning paradigm to federated learning, and the definition of methodological guidelines for developing artificial intelligence services based on federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases. • We analyse the most recent federated learning and differential privacy frameworks. • We present Sherpa.ai FL, a unified framework for federated learning and differential privacy. • We discuss the adaptation of machine learning models to federated learning. • We define the guidelines for privacy-preserving artificial intelligence services. [ABSTRACT FROM AUTHOR]

Complex experiments such as the stellarator Wendelstein 7-X demand efficient experimental planning, operation, and data evaluation. To assist the process organization at W7-X, a metadata framework has been implemented. It annotates a physics program with tags describing its intention, main parameters, boundary conditions, and evaluation results. The approach enables relevant metadata to be collected exactly where information is available and to forward it from experimental planning and execution to the related entry in the central W7-X Logbook. In consequence, these metadata offer possibilities for classifying, searching, filtering, and so on, both in the database of the prepared programs and in the logbook of the executed programs. Wherever possible, metadata are generated automatically. Starting from the parameters of the program, the framework allows typical physics or technical quantities or any programmatically extractable information describing the programs intention to be extracted already while editing. During discharge preparation, metadata are retrieved from the related session planning documents and from the actual session environment. Supplementary tags can be added manually from an extendible tag catalog to categorize experimental programs or to provide metadata for not fully integrated diagnostics. All metadata are logged together with status and progress information during execution and are available within the program’s log—where the metadata can be finally completed, for example, to manually qualify the success of the program or by adding programmatically produced metadata using the logbook’s programming interface. [ABSTRACT FROM AUTHOR]

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

In previous works the author has proposed a universal software framework that allows implementing the known algorithms of group method of data handling, model bases, training methods and model selection criteria. This paper introduces the solution of a topical problem of increasing the performance of the framework. Based on the review of existing computing architectures for parallel data processing and software systems for inductive modeling supporting parallel computations the author has worked out the requirements for the subsystems of parallel computing and memory management of the software framework. Using the methodology of object-oriented analysis and design the author developed the object-oriented structure of these subsystems and introduced the specifics of their operation on each of the mentioned computing architectures. The performance of the parallel implementation of the combinatorial group method of data handling algorithm on basis of the software framework was evaluated experimentally for multi-core processors.

The relevance of the work is caused by necessity of increasing efficiency of automatic data mining systems based on meta-learning. The main aim of the study is to design an automatic meta-learning system supporting selection of optimal algorithm for problem solving and calculation of optimal parameters of its functioning. The methods used in the study: inductive modeling, methods of statistical analysis of results. Results: The known meta-learning systems were integrated based on produced classification features taking into account internal structure of systems. The author has stated the requirements for implementation of the automatic meta-learning system and has offered the way to build a meta-learning system satisfying all stated requirements and accumulating meta-knowledge, building meta-models on its basis, selecting optimal algorithm from a set of available ones and calculating optimal parameters of its functioning. The object-oriented architecture of a software framework for implementation of any meta-learning system presented in the systematization was developed. The efficiency of the implemented automatic meta-learning system using algorithms of group method of data handling was experimentally examined being applied to solution of problems related to the short-term time series forecasting (1428 time series from the testing set known as "M3 Competition").

Blockchain applications in healthcare have grown rapidly. They include record-keeping, clinical trials, medical supply chains, patient monitoring, etc., where blockchain characteristics are needed to improve safety, privacy, and security. Blockchain technology is one of the most significant disruptive technologies today. However, Porru et al. 1 reported that it lacks processes, tools, and techniques. Therefore, this paper provides a systematic framework for a secure and sustainable software engineering framework for healthcare blockchain applications (S 3 EF-HBCA). S 3 EF-HBCA is a significant contribution that includes requirements engineering for healthcare, business process modeling for healthcare, domain modeling for healthcare, a reference architecture for healthcare, and validation by a case study on electronic healthcare record management system (EHR), and simulation with business process modeling notation (BPMN) tools. The simulation shows it has taken 10.45 min to process 100 instances of real-time data and service requests. The overall result shows encouragement regarding process, tools, standards, and testing., (© 2023 The Authors.)

As the complexity of automated driving systemss (ADSs) with automation levels above level 3 is rising, virtual testing for such systems is inevitable and necessary. The complexity of testing these levels lies in the modeling and calculation demands for the virtual environment, which consists of roads, traffic, static and dynamic objects, as well as the modeling of the car itself. An essential part of the safety and performance analysis of ADSs is the modeling and consideration of dynamic road traffic participants. There are multiple forms of traffic flow simulation software (TFSS), which are used to reproduce realistic traffic behavior and are integrated directly or over interfaces with vehicle simulation software environments. In this paper we focus on the TFSS from PTV Vissim in a co-simulation framework which combines Vissim and CarMaker. As it is a commonly used software in industry and research, it also provides complex driver models and interfaces to manipulate and develop customized traffic participants. Using the driver model DLL interface (DMDI) from Vissim it is possible to manipulate traffic participants or adjust driver models in a defined manner. Based on the DMDI, we extended the code and developed a framework for the manipulation and testing of ADSs in the traffic environment of Vissim. The efficiency and performance of the developed software framework are evaluated using the co-simulation framework for the testing of ADSs, which is based on Vissim and CarMaker.

In this paper, we present an intelligent and flexible framework for autonomous pick-and-place tasks in previously unknown scenarios. It includes modules for object recognition, environment modeling, motion planning and collision avoidance, as well as sophisticated error handling and a task supervisor. The framework combines state-of-the-art algorithms and was validated during the first phase of the European Robotics Challenge in which it obtained first place in a field of 39 international contestants. We discuss our results and the potential application of our framework to real industrial tasks. Furthermore, we validate our approach with an application on a real harvesting manipulator. To inspire other teams participating in the challenge and as a tool for new researchers in the field, we release it as open source. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) is typically composed of spatially distributed miniature sensors that help collect large amounts of real-time environmental data from buildings. These environmental data (e.g., temperature, humidity, CO2 concentration) can assist a series of heating, ventilation, and air conditioning (HVAC) equipment to increase the building energy efficiency. From a system design perspective, heterogeneous wireless sensor networks need to address two challenges. First, sensor data acquisition, conversion, fusion, and packaging involve a series of software processing. Since each type of environmental sensor typically has unique processing requirements, it is difficult to develop an efficient software framework to combine the processing of multiple heterogeneous sensors. Second, during normal operation of a heterogeneous wireless sensor network, if users insert or remove some environmental sensors, the entire WSN system should operate normally. In this work, in order to solve the above two system design challenges, we have developed a low-power, low-cost, small form-factor WSN development platform, and its software framework can perform efficient data acquisition, conversion, fusion, and packaging for multiple heterogeneous sensors. Our proposed software framework enables easy and rapid WSN system deployment without affecting the overall functionality of each node. The proposed design differs from existing WSN platforms in that it emphasizes advanced high-level usability and reduces time to market without sacrificing low-level features. The proposed WSN system has been implemented and tested in an office building for indoor fire hazard detection. The experimental results show that our software framework can successfully complete data acquisition, conversion, fusion, and packaging tasks for three heterogeneous environmental sensors. In addition, we have verified that our software framework supports robust system operation when inserting or removing sensors from an existing heterogeneous WSN system. [ABSTRACT FROM AUTHOR]

A lack of consistent data, reliable analysis techniques, and user-friendly tools is inhibiting decision makers' abilities to assess and evaluate the sustainability performance of manufacturing processes and systems. Recent integration of information technology with advanced manufacturing technologies has led to a new paradigm, termed smart manufacturing or Industry 4.0. To leverage this paradigm shift for the advancement of sustainable manufacturing, efforts have been made to characterize unit manufacturing processes (UMPs) for sustainability assessment and to develop frameworks, methodologies, and standards utilizing information modeling approaches. This paper proposes a grey box object-oriented software framework for sustainability assessment of complex manufacturing systems, utilizing the multiple inheritance principle for composition of UMP models. Information modeling guidelines based on the framework are provided for UMP model developers and UMP performance analysts to construct and analyze system models. To demonstrate the usefulness of the framework and guidelines, a prototype information model is developed for a hybrid manufacturing system composed of fused deposition modeling (an additive process) and CNC milling (a subtractive process). [ABSTRACT FROM AUTHOR]